May198£
                          Do not remove. This document
EPA-450-4-88-007              should be retained in the EPA
                          Region 5 Library Collection.
            Statistical Properties Of Hourly
        Concentrations  Of Volatile Organic
      Compounds At  Baton Rouge,  Louisiana
                                 By:
                             Alison K. Pollack
                             Thomas J. Permutt
                              Mithra Moezzi

                           Systems Applications, Inc.
                            San Rafael, CA 94903
                           Contract No. 68-02-4331
                      EPA Project Officers: William F. Hunt, Jr.
                                     Robert B. Faoro
                     U.S. ENVIRONMENTAL PROTECTION AGENCY
                           Office of Air and Radiation
                     Office of Air Quality Planning and Standards
                     Research Triangle Park, North Carolina 27711
                                U.S.              ion Agency
                                                  12ft Floor
                                Chicago, IL  60604-3590

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This report has been reviewed by the Office of Air Quality Planning And Standards, U S Environmental
Protection Agency and approved for publication as receivec from the contractor Approval aoes net signify
tnat the contents necessarily reflect the views and policies of the Agency,  neither does mention  of trade
names or cortimercial products constitute endorsement or recommendation for use
                                       EPA-450/4-88-007

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                               Contents



Acknowledgements	    i

Excutive Summary 	    ii

1  INTRODUCTION	    1

2  MONITORING SITE AND DATA BASE DESCRIPTION	    2

3  DISTRIBUTIONS OF HOURLY CONCENTRATIONS	    5

4  RELATIONSHIPS BETWEEN MEAN AND PEAK CONCENTRATIONS	   10

5  ANALYSIS OF VARIANCE MODELS	   19

6  CONCLUSIONS	   27

References	   28

Appendix A: Summary Statistics and Lognormal Probability Plots

Appendix B:  Scatterplots of Daily Peak 1-Hour, 3-Hour, and
            8-Hour  Average Versus Daily Mean  Concentrations

Appendix C: Analysis  of Variance  Tables and Least Squares Means
8715^1  1

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                              Acknowledgements

The authors would like to thank Gustave von Bodungen and Jim Haziett of the Loui-
siana Department of Environmental Quality for making available the data discussed
in this report, for providing information on the monitoring site and methods, and for
valuable assistance in data interpretation.  We also acknowledge the assistance of
Bob Johnson, our in-house computer expert, for transporting the data from the
Louisiana DEQ computers.
 87lS»+rl

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                            EXECUTIVE SUMMARY
Hourly measurements of 16 volatile organic compounds (VOC) in downtown Baton
Rouge, Louisiana have been monitored since 1984. These are probably the most
extensive data of hourly air toxics concentrations in the  United States collected to
date.  In this report we discuss the  results of an exploratory data analysis of nearly
two years of hourly concentrations. Although we analyzed the data for all VOCs
monitored,  and present our conclusions for all data, our discussion  centers on
benzene, a  known human carcinogen.

The hourly  concentration distributions are extremely heavy-tailed; most  of the
concentrations are low, but there are a fair number of very high concentrations.
Most of the pollutants are well characterized by a lognormal distribution.

The data set provides a unique opportunity to see how  well daily maximum one-hour
concentrations can-be predicted by 24-hour averages.  At lower concentrations there
is a strong  correlation between the maximum hourly average and the  24-hour mean.
At higher concentrations, however, the relationship is  much weaker.  Twenty-four
hour means can indicate days on  which very high  hourly concentrations occur, but
cannot reliably predict the magnitude of these peaks.  Daily maximum three-hour
and eight-hour averages are more highly correlated with  24-hour means,  and can
therefore be predicted more reliably  by 24-hour means.

Analysis of variance models were fitted to the data to see  if the variability in the
concentrations can be explained by wind direction, hour of the day, day of the week,
and month. All four variables were found to be statistically significant,  but explain
only a small percentage of the total variability in the measurements.   From the
analysis of  variance modeling, concentration averages by hour, wind direction, day,
and month  were calculated; the averages for benzene were plotted. Benzene
concentrations peak during morning and evening rush hours, are  low on weekends
(especially  Sundays), and are highest  in January.  Most interesting  are the patterns by
wind direction. Benezene concentrations are highest when the winds  are from  the
north, presumably attributable to a large chemical industrial complex north of Baton
Rouge.  The results of the analysis  of variance for all VOCs are  similar to tnose for
benzene.
87 1

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                              1  INTRODUCTION
The Louisiana Department of Environmental Quality has measured concentrations of
16 species of volatile organic compounds hourly at the state capital in Baton Rouge
since  1984.  These are probably the only such extensive data on .hourly concentrations
of volatile organic compounds in the  United States. The aata ser provides an oppor-
tunity for interesting analyses of ambient volatile organic compounds concentra-
tions. In this report we present the results of an exploratory data analysis.

Our analysis of  these data is focused largely on the possibilities for making
inferences about hourly concentrations from more limited data.  In  Section 2 we
describe the monitoring methods and discuss the major emission  sources that influ-
ence the site. In Section 3 we examine the distribution of the  hourly measurements,
giving particular attention to the higher concentrations.  In Section 3 we discuss the
extent to which peak short-term concentrations can be predicted from 24-hour
average concentrations.  In Section 4 we consider how well hourly data can be pre-
dicted on the basis of seasonal, diurnal, and meteorological patterns.  We conclude by
discussing potential future analyses of this unique data set.
8 7 1 5^r 1  2

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             2  MONITORING SITE AND DATA BASE DESCRIPTION
Since 1984 the Louisiana Department of Environmental Quality has been collecting
hourly measurements of volatile organic compounds (VOC) in downtown Baton Rouge
near the state capitol.  The monitor is based in an office building and collects
ground-level measurements. Several major sources of emissions are within a few
miles of the monitoring site. A major state highway circles  the downtown area about
a mile to the south and east. There are fuel transfer operations on Mississippi  River
barges to the northwest.  Most important, there is  a large  industrial complex due
north of the site, stretching from about one and one-half mile away to about three
miles away. Located in this complex are two oil refineries and five chemical plants.

Sixteen volatile organic compounds are measured hourly by dual computer controlled
gas chromatographs. Eight hydrocarbon species are measured by a flame ionization
detector (FID). They are benzene, toluene, ethyl benzene, m-xylene, propane,
butane, hexane, and pentane.  In addition, unknown and total hydrocarbons are  mea-
sured. Eight chlorinated hydrocarbon species are measured by a Hall electrolytic
conductivity detector (HECD). They are vinyl chloride, methylene chloride,  1,1-
dichloroethane, chloroform, ethylene, dichloride, carbon tetrachloride, trichloro-
ethylene, and perchloroethylene. Unknown chlorinated hydrocarbons and total
chlorinated hydrocarbons are also determined. Meteorological parameters collected
hourly are wind speed, wind direction, and ambient temperature.

Although the concentrations are reported as hourly averages, the actual period of
continuous air intake sample collection is 20 mintues for the FID and 25 minutes for
the HECD.  The remaining portion of the hour is required for gas chromatograph
sample analysis.  The instrumentation is automatically calibrated every day between
1100 and 1400, so only 21 hourly samples per day are actually collected.  The mea-
surement system and the quality assurance features are described in detail.elsewhere
(LADEQ, 1986).

Although the monitoring site has been in operation since 1984, construction near the
monitoring site until September 1985 was thought  to result in anomalous  and
unreliable measurements; we therefore excluded these observations. In this  analysis
we examined over  11,000 hourly measurements taken between 3 October 1985  and 30
June  1987.
 87 1

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Figure 2-1 shows the relative abundance of each of the hydrocarbon species mea-
sured, based on average hourly  ppbv concentrations.  The largest proportion, 33 per-
cent, comprises unknown hydrocarbons. Of the identifiable hydrocarbons, propane
and butane each account for 18 percent of the total. Next in abundance are toluene
and benzene, which account for 13 and 10 percent, respectively, of total hydrocar-
bons.  In this report we concentrate mainly on the hourly concentrations of benzene,
a known human carcinogen.
 87 1 5>*r 1  2

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                                                  Propane
 Unknown
 hydrocarbons
M-xylene
Ethyl benzene

        Toluene
                                                               Butane
          FIGURE 2-1.  Hydrocarbon composition by species
          (based on average hourly ppbv concentrations).

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              3  DISTRIBUTIONS OF HOURLY CONCENTRATIONS
The most basic and one of the most useful ways to analyze the approximately 11,000
hourly measurements of each species is to try to describe the statistical distributions
of the concentrations.  This will provide direct answers to such questions as, "How
often is the hourly concentration of benzene above 10 ppbv?" Furthermore, a sta-
tistical characterization of these concentration distributions might be useful in mak-
ing inferences about hourly concentrations at a site where concentrations are not
measured hourly.

The distribution  of benzene concentrations is fairly typical.  A cumulative frequency
distribution for benzene concentrations below  10 ppbv is presented in Figure 3-1  and
summary statistics are listed below in Table 3-1.
        TABLE 3-1.  Summary statistics for benzene.


        No. of hourly observations = 11,306

        Mean = 5.76  ppbv

        Standard deviation = 20.20 ppbv

        Minimum = 0.0 ppbv (21%)
        Percentiles (ppbv):

               25th percentile = 0.6
               50th percentile = 1.8
               75th percentile = 4.1
               90th percentile = 10.3
               95th percentile = 20.2
               99th percentile = 82.89

        Highest observations (ppbv):

               708     Jan. 25, 1986
               494     Mar. 4, 1986
               462     Mar. 6, 1986
               408     Jan. 13, 1986
               378     Jan. 13, 1986
 87 1 5*r1  2

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    100.0%
o
c
0)

cr
03
03


JS
3
E
3
O
              01     23456789    10
                               Benzene, ppbv
      FIGURE 3-1.  Cumulative frequency distribution of benzene

      concentrations less than 10 ppbv.

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Over 20 percent of the measurements are zero: in a large orooortion of the hours r.o
benzene was detected at all.  Many more measurements are not much above zero:
the lower quartile is 0.6 ppbv, the median is 1.8 ppbv, and 90 percent of the mea-
surements are less than 10.3. The highest measurements  are very far from zero
indeed:  the maximum is 708 ppbv, four measurements are over 500 ppbv, and about
100 are over 100 ppbv.  The five highest values all occur in January or March, and
two of the five highest values are on the same January  day in 1986.  A cause for
unusually high concentrations is sought by LADEQ staff when they occur.  In most
cases, the highest concentrations are associated with an unexpected release from one
of the nearby chemical  facilities.

The summary statistics in Table 3-1 indicate that the distribution of  benzene concen-
trations is characterized by low levels for the  majority of the hours but very high
levels for a small proportion of the hours.  Such a heavy-tailed distribution is com-
mon for hourly pollutant measurements, which can often  be well described by a log-
normal distribution (Ott, 1986).  Figure 3-2 is a lognormal probability plot of the
concentrations of benzene. The vertical axis is the van der Waerden  normal score, or
the value from the standard normal distribution that would be expected to have a
given rank. For example, the 84th percentile is assigned  a normal score of one
because one standard deviation above the mean of a normal distribution is the  84th
percentile. The horizontal axis is the common logarithm of the concentration.
Values of zero were assigned a logarithm of -1.5.  If the points in a lognormai proba-
bility plot fall along a straight line, we can conclude that the measurements do
indeed arise from a lognormal distribution.

In viewing Figure 3-2 it should be kept in mind that the single point at  abcissa -1.5
represents about 20 percent of the data, and the other  Z's in the lower  portion of the
curve also represent many observations each.  The figure therefore shows mainly the
upper tail of  the distribution.

The points are in a fairly straight line over a considerable range, from  benzene con-
centrations of less than 1  ppbv (log 1  = 0) to the maximum concentration of 708 ppbv
(log 708 = 2.85). Thus the lognormal distribution  is a fairly good fit to  the empirical
distribution in this range. The bending of the  curve at  concentrations below 1 ppbv is
perhaps not too important.  Although there are a  great many concentrations in this
range, they are only reported to one significant figure, and may be close to the limit
of detection.  If the  measurements  were reported to two or three significant digits
(as the higher concentrations are), then the bending of  the curve at concentrations
below 1 ppbv might not occur.

This distributional fit offers  the possibility of getting at  Least  a rough  idea of  tne
behavior of the extreme hourly concentrations from less extensive data.  Suppose
that only a few hundred hourly concentrations were available to estimate the  90th
and 95th percentiles.  This would be sufficient to estimate the 90th and 95th percen-
tiles fairly accurately. Assuming the logarithms  of the concentrations are normal
 87 IS^r 1 2

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                                                                   CRIK
                                                              22 !
                                                            V22D
                                                           222
                                                         2221
                                                       L i 1 1
                                                      222
                                                    222
                                                   222

                                                 22
                                                22
                                               22
                                              2
                                             22
                                            22
                                          2:2
                                         222
                                        22
                                       2
                                    2 2 2
                            2222
». "t    Sbu> 06i
                                    or         o i


                                          loglO 6rfU ere
  FiGUHE 3-2.  Lognonrai probability plot  of hourly benzene concentrations.
   ("A"  = one observation, "B"  = two observations; etc.;  "Z" = 26 or more
  observations.)

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with mean u and 'standard deviation ^, the following simultaneous equations could be
solved for u and c:

       u •*• 1.2&o  = log PQQ and

       u + 1.65c  = log
This amounts to fitting a straight line through the 90th and 95th percentiles on the
probability plot.  The estimated values of y and a could then be used to estimate
fairly extreme quantiles. For example, the characteristic monthly maximum, the
value that is exceeded one hour per month in the long run, can be estimated by the
upper 1/720 quantile of the fitted lognormal distribution.  For a standard normal dis-
tribution, the 1/720 upper quantile is 2.9S.  The characteristic monthly maximum
could be estimated by the antilogarithm of u + 2.98o after u and a have been esti-
mated.

As a numerical example, consider benzene. As shown  in Table  3-1, the observed 90th
and 95th percentiles are  10.3 and 20.2 ppbv. This gives u = 0.00 and a  = 0.79.  The
characteristic monthly maximum would  therefore be estimated as log" [0.00 +
(2.98-0.79)] = 227 ppbv.  In  fact, the observed upper 1/720 quantile is 234.  The esti-
mated characteristic monthly maximum is very close indeed to that predicted by this
simple logarithmic fit to the data.  This is not the case for all of the VOCs. For
example, the actual characteristic monthly maximum  for butane, 352,. is a factor of
two larger  than the estimate of 186.  This gives a fair picture of the limitations of
the technique. Obviously this method of estimation is not accurate  enough to
determine compliance with a standard.  However, for a distribution  spanning a range
of concentrations from zero to hundreds of times the median, the method may pro-
vide a rough estimate of the magnitude  of extreme events not otherwise available.

Summary statistics and lognormal probability plots for the other species are in
Appendix A.  The results are substantially similar to those for benzene.
 87 1 5"+r 1  2

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         RELATIONSHIPS BETWEEN MEAN AND PEAK CONCENTRATIONS
The relationships between the 24-hour mean and daily maximum one-hour, three-
hour, and eight-hour average concentrations were investigated in order to assess how
well measurements of 24-hour concentrations can represent daily short-term peaks.
The short-term maxima were computed using  fixed block averaging; for example,
eight-hour concentrations were averaged for midnight to 0700, 0800 to 1500, and
1600 to 2300.

Correlation coefficients between daily average concentrations and the daily maxi-
mum one-hour, three-hour, and eight-hour average concentrations for each species
studied are listed in Table 4-1. The correlations between daily average and peak
one-hour concentrations range from a low of 0.53 for trichloroethylene to a high of
0.97 for carbon tetrachloride; most correlations are between 0.70 and 0.90.  For
benzene, the correlation between daily average and  peak one-hour concentrations
(based  on 582 pairs of available data) is 0.85.  Thus,  100(0.85)  = 72 percent of the
variance about the mean  of peak one-hour concentrations can be explained by the 24-
hour average concentration.

The correlation coefficient of 0.85 for benzene is considerably higher than the 0.65
determined in  an earlier analysis of the Louisiana data (Permutt and Edland,  1987).
The earlier study used data between January 1985 and mid-July 1986, while the study
reported here  uses data between October 1985 and June 1987.  As noted above,
observations before October 1985 are believed to be anomalous; their inclusion in the
earlier study and exclusion from the current study may account for the noted dis-
crepancy in correlation coefficients.

As one would expect, correlations generally increase as averaging periods for the
peak increases. Correlations between 24-hour average and peak eight-hour concen-
trations are above 0.90 for most species.  In general, correlations between 24-hour
mean and peak 1-, 3-, and 8-hour concentrations  for the hydrocarbons are higher than
those for the volatile organic compounds.

Scatterplots of daily mean benzene concentrations against peak 1-hour, 3-hour, and
8-hour  concentrations are shown in Figure 4-1. In all of the plots a line with slope
equal to the number of short-term averaging periods in one day's data forms  a
natural upper bound for the ratio of the peak short-term concentration to daily mean
concentration. A full set of  plots for all species  is included in Appendix B.
                                    10

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       TABLE 4-1.  Correlations of daily mean and  peak  1-,  3-,
       and 8-hour concentrations.
Compound
n-Propane
n-Butane
n-Pentane
n-Hexane
Benzene
Toluene
Ethyl Benzene
m-Xylene
Unknown HC
Total HC
Vinyl Chloride
Methylene Chloride
Dichloroethylene
Chloroform
Ethylene Bichloride
Carbon Tetrachloride
Trichloroethylene
Perchloroethylene
Unknown VOC
Total VOC
1 Hour
0.65
0.88
0.86
0.78
0.85
0.88
0.90
0.82
0.83
0.81
0.84
0.83
0.74
0.67
0.85
0.97
0.53
0.82
0.71
0.72
3 Hours
0.80
0.93
0.92
0.86
0.88 '
0.92
0.93
0.88
0.89
0.89
0.86
0.80
0.63
0.65
0.84
0.94
0.55
0.76
0.76
0.68
8 Hours
0.94
0.97
0.97
0.94
0.96
0.97
0.97
0.94
0.95
0.95
0.94
0.90
0.85
0.93
0.91
0.97
0.87
0.86
0.38
0.38
                                  11

17 l 5»tr  3

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  FIGUPE 4-la.   Daily mean benzene concentrations  (ppbv)  versus daily  peak
  one-hour concentrations.  ("A" = one observation;  "B" = two observations;
  etc.;  "Z" =  26 or more  observations.)
                                    12

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                                A
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MO'C     J5C DBS MlDO[N
 FIGUPE 4-Ib.   Daily mean benzene concentrations  (ppbv) versus daily
 peak three-hour concentrations.

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 100

A
A A
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  1C -                    A
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        ?8 oes HIOOEK
FIGURE 4-lc.   Daily  mean benzene concentrations  (ppbv) versus daily
peak  eight-hour concentrations.

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The peak short-term benzene concentrations show considerable scatter ir the 'jpper
fifth percentile of daily means. In the lower range of the data the linear reiauonsnip
between daily mean and peak shori-term concentrations is relatively strong.  Plots
for many of the other species exhibit similar patterns. As can be seen from the
plots, linear regression predictions of peak concentrations based on the daily mean
concentrations would substantially overpredict or underpredict many of  the observed
values in the high concentration range.  In particular, the two highest observed one-
hour concentrations, 708 ppbv and 494 ppbv, would be grossly underestimated.

In general, the peak three- and eight-hour benzene concentrations show  less scatter
than the one-hour concentrations, as would be expected.  Regression predictions of
the two highest observed three-hour concentrations would be substantially under-
estimated.  Regression predictions of the highest eight-hour benzene concentrations
would be fairly close to the observed values.

The extreme points may strongly  influence the correlation coefficient, since they
contribute heavily to the covariance between the daily mean and peak short-term
concentrations.  Since, as we observed in the previous section, the distributions of
hourly concentrations are very heavy-tailed and fit reasonably well to a lognormal
distribution, a logarithmic transformation of the short-term maxima will reduce the
influence  of the extreme observations on the correlation and hence the  regression.
Scatterplots of logarithmic daily  mean benzene concentrations against logarithmic
daily peak 1-hour, 3-hour, and 8-hour concentrations are shown in Figure 4-2.  All
three scatterplots show much stronger correlations than the untransformed data.
Regression predictions would still underestimate the highest one- and three-hour
daily maxima, but not as substantially as the regressions based on the untransformed
averages.

In summary, correlation coefficients between daily mean and short-term maximum
concentrations are high; but if a regression line  were drawn, the  peak concentrations
in the upper ranges of concentration would be highly scattered about the line. Due
to this variability, a linear regression model may often not provide accurate predic-
tions of the highest one-hour concentrations from 24-hour concentrations.  Taking
logarithms of the short-term averages strengthens the correlations and improves the
accuracy  of the regression predictions, but still may not provide  accurate enough
predictions of peak short-term concentrations.
 87 1 5-*r 1  2                           15

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FIGURE 4-2a.   Log  daily mean benzene  concentrations (ppbv)  versus daily
peak one-hour concentrations.   ("A" = one observation;  "3"  = two
observations;  etc.;  "Z" = 26 or more  observations.)
                                      16

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  FIGURE  4-2b.   Log  daily mean benzene concentrations  (ppbv)  versus  daily
  oeak three-hour concentrations.

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2.0
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1.0
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FIGURE 4-2c.   Log  daily mean benzene concentrations  (pphv)  versus  daily
peak  eight-hour concentrations.
                                        18

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                     5  ANALYSIS-OF-YARIANCE MODELS
In an earlier analysis of the Louisiana data, concentrations of toxic gases averaged
for each combination of hour and wind direction were examined; a two-way analysis-
of-variance model was fit to describe the variation of concentrations with hour of
the day and wind direction (Permutt et al., 1987). Availability of tne full hourly data
set now allows several related issues to be investigated. First, some extra explana-
tory variables can be added to the model, e.g., the effects of season and day of the
week.  Second, it  is possible to transform the data; in view of the extreme skewness
and reasonable lognormal fit, a logarithmic transformation might be aesirabie.
Finally, and perhaps most important, it is possible not only to estimate the para-
meters of the best-fitting model but also to judge how well the  model  fits the
individual hourly measurements.

We fit a four-way anaiysis-of-variance model to the hourly concentrations of each of
the 20 species using SAS  PROC GLM.  The four classification variables were hour of
the day, wind direction, day of the week, and month. The hours range from zero
(0000 to 0100) to  23 (2300-2400); three midday hours are usually missing because of
instrument calibration and some data are erroneously identified as "hour  81". Wind
direction was converted from degrees into  16 compass points.  A direction of 0
degrees (due  north or DN) was treated as a separate category because calm winds
were sometimes recorded as direction 0, and this  is different from north  winds.
There  is also an error category (ERR) for a few data with direction above 360
degrees.  The days of the week are represented as 1 to  7, starting from Sunday, and
seasonal  effects are represented by calendar month. We  thus have a linear model
with 25+ 18  + 7 + 12 = 62 parameters, which are estimated simultaneously by linear
least squares. Because the design is unbalanced (e.g., there  are more  east winds in
some hours than others) the parameter estimates are not simple averages.  The
average concentration for a given hour (or wind direction, day of the week, month),
controlling for the effects of the other variables, is estimated by the  "least-squares
mean" for that hour (or other variable).

Table  5-1 is the analysis-of-variance table for benzene. Each of the four main
effects is significant at the 0.01 level, indicating that benzene  concentrations cer-
tainly differ  among hours, days, seasons and wind directions. Perhaps most remark-
able is the R  value of 0.09.  These effects, though highly significant, account for
only a small  fraction of the variablity in benzene concentrations.  We are therefore
left with enormous variation in hourly concentrations that cannot be explained by
seasonal, diurnal, weekly or meteorological effects and appears to be  random.

Considering the extent of the range of concentrations, the skewness of the distribu-
tion over this range, and the reasonable lognormai fit to  the distribution, it seems

 87 15>*r 1   2                            19

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reasonable to try fitting the logarithms of the concentrations, rather than the con-
centrations themselves, as functions of the same four sets of explanatory variables.
This is equivalent to assuming a multiplicative rather than additive relationship ,
betweeen the effects of wind speed and hour, for example.  The zero concentrations
present a problem in taking logarithms, of course; we somewhat arbitrarily took the
logarithm of 0 to be -1.5.

Table 5-2 is the analysis-of-variance table for log benzene.  The  R  value  is 0.1S, as
compared with 0.09 for the untransformed benzene concentrations.  This logarithmic
fit may therefore be a slightly better way to describe the seasonal, diurnal, and
meteorological patterns of concentrations, but most  of the variation in the concen-
trations still remains unexplained.

The least squares means for benzene are  plotted by hour, wind direction, month, and
day in Figures 5-1 through 5-4, respectively. These plots show the least squares
means resulting  from  fitting the model to both the transformed and untransformed
data.  The least  squares means from the transformed data were back-transformed to
the original units and  are thus geometric means. The geometric means are always
lower than  the arithmetic means  because they are much less influenced by the higher
concentrations.

Concentrations tend to be highest in the  morning peak traffic period, as shown by the
relatively high least-squares means for hours 6 and 7 in Figure 5-1.  Concentrations
are also relatively high during hours 20 and  21 just past the evening rush hour.  It is
interesting to note  that in the hour immediately after the midday calibration period
concentrations are  higher relative to the following few hours. This may indicate
higher concentrations during the  missing midday hours, when presumably traffic
increases.

Northerly winds are generally associated with relatively  high benzene concentra-
tions. North of  Baton Rouge is a large complex of refineries and chemical plants.
Concentrations tend to be relatively high in January (three of the five highest
recorded benzene concentrations occurred in January).  Sundays have relatively low
concentrations,  as would be expected.

It should be emphasized again that these effects, while highly significant, account
for very little of the variation in benzene concentrations.  For example, the  highest
hourly least-squares mean is 9.9 ppbv, and the lowest is 3.6 ppbv.  This would appear
to be a significant  difference, practically as well as statistically.  Nevertheless,  it
does  not go far toward explaining concentrations as  high as 500 ppbv.

The analysis-of-variance tables and least squares means  for all species are in Appen-
dix C. The results are generally  similar  to  those for benzene.
                                      21
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                                        Arithmetic mean
                                        Geometric mean
        0    2   4   6   8   1012141618202224
                        Beginning hour
FIGURE 5-1. Estimated mean benzene concentrations (ppbv) by hour.
                              23

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       i  •  r  '  i  '  r  T  r ~~"i  '  I  T  i  •  r  '  I  • T '  I  •  I  •  i—•  I  •  i  '	1—'  I


      DN   N  NNE NE ENE  E  ESE SE SSE S  SSW SW WSW  W WNW NW NNW  N
                                 Wind Direction
   FIGURE 5-2.  Estimated mean benzene concentrations (ppbv) by wind direction.
                                    24

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 FIGURE 5-3.  Estimated mean benzene concentration (ppbv) by month.
                               25

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FIGURE 5-4. Estimated mean benzene concentrations (ppbv) by day of week.
                            26

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                               6  CONCLUSIONS
The Louisiana data offer significant insights into the patterns of atmospheric con-
centrations of volatile organic compounds.  However, our exploratory analysis leaves
important questions unanswered.

The distributions of VOCs at Baton Rouge are characterized by an impressive varia-
bility.  A large proportion of the hourly concentrations are recorded as zero; many
observerations are in the range from 0.1 to 1.0 ppbv; and several observations are in
the thousands of parts per billion. In most cases this variability can reasonably  be
compared with that of a lognormal distribution.  This comparison can be used to give
a rough idea of the probable maximum hourly concentrations over the course of a
month, for example. However, without a better fit or a theoretical reason for the
distribution to be lognormal, there remains considerable uncertainty about the
general applicability of this technique.

Peak hourly concentrations for a day are fairly highly correlated with average con-'
centrations for the day or part of the day.  This suggests that measuring, say, 24-
hour mean concentrations will give a fairly good idea of which days have high peak
concentrations.  However, it would be very difficult to estimate the magnitude of
the peak concentrations from  the 24-hour means.

Temporal and meteorological patterns are evident in the data.  There are significant
daily and weekly cycles and wind direction  effects. However, these effects account
for only a small fraction of the variability in the hourly measurements.

We have thus analyzed some of the variation in concentrations of volatile organic
compounds in ways that may be useful for various purposes. Our understanding is at
present limited, however, and  we must conclude that these concentrations have a
quite large unexplained variation. The peak concentrations could be studied in
further detail.  It would be useful to know whether  these individual  hourly peaks
coincide with identifiable combinations of day, time, season, and wind direction.
Statistical classification techniques could be appropriately applied to this problem,
rather than the analysis of variance techniques that were used to study average con-
centrations.
 67 1
                                      27

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                                  References
LADEQ.  1986.  "Quality Assurance Project Plan for the Monitoring of Volatile
     Organic Compounds." Louisiana Department of Environmental Quality, Office
     of Air Quality and Nuclear Energy.

Ott, W. R.  1988.  "A Physical Explanation of the Lognormality of Pollutant Concen-
     trations." Annual meeting of the Air Pollution Control Association, Dallas,
     Texas.

Permutt, T. 3., S. D. Edland.  1987.  "Statistical Analyses of Hourly Benzene
     Readings." Systems Applications, Inc., San Rafael, California (SYSAPP-
     87/157).

Permutt, T. 3., M. Moezzi, A. B. Hudischewskyj, and C. S. Burton.  1987. "Statistical
     Analysis of Concentrations of Toxic Air Pollutants in California and
     Louisiana.  Systems Applications, Inc., San Rafael, California (SYSAPP-
     87/131).
                                     28
 8715^1  2

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                     Appendix A




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                           Appendix C




      ANALYSIS OF VARIANCE TABLES AND LEAST SQUARES MEANS
371S^f1  1

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                                   TECHNICAL REPORT DATA
                            /Phase r<_~d Ins;ruc;ioi:s (..n the rc:ers< Ui fort- co
i  REPORT NO

EPA-45Q/4-88-QD7
                                                           |3 RECIPIENT'S ACCESS'ON NC
          I
4 TITLE AND SUBTITLE
 Statistical Properties of  Hourly  Concentrations of
 Volatile Organic Compounds At  Baton  Rouge, Louisiana
          6 PERFORMING ORGAN!ZAT ON CODS
          5 REPOR" DATE
             May  1938
7 AUTHOR(S)
  Alison K. Pollack, Thomas  J.  Permutt and Mithra Moezzi
                                                           a. PERFORMING ORGANIZATION
9 PERFORMING ORGANIZATION NAME AND ADDRESS
 Systems Applications,  Incorporated
 101  Lucas Valley Road
 San  Rafael, California 94903
                                                            10 PROGRAM ELEMENT NO
          11 CONTRACT GRANT ^


            68-02-4331
12 SPONSORING AGENCY NAME AND ADDRESS
Monitoring and Reports  Branch   (MD-14)
U.S.  Environmental Protection  Agency
Research Triangle Park,  NC  27711
                                                            13. TYPE OF REPORT AND PERIOD CO v£ = E;
          14 SPONSORING AGENCv COCH
15 SUPPLEMENTARY NOTES
 EPA Project Officers:  William F.  Hunt, Jr., and Robert  B.  Faoro
16 ABSTRACT
 Hourly measurements of  16  volatile organic compounds  (VOC)  in downtown Baton
 Rouge, Louisiana have been monitored since 1984.  These  are probably the most
 extensive data of hourly air  toxics concentrations  in  the  United States collected  to
 date.  In this report,  we  discuss the results of  an exploratory data analysis  of
 nearly two years of hourly concentrations.  Although we  analyzed the data, the
 discussion centers on benzene,  a known human carcinogen.
17
a
Statistical
VOC
Benzene
Carcinogen
Baton Rouge
18 DISTPI8UTIOI1
Unl i mi ted
EPA Form 2220-1
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
Properties
Hourly Monitoring
Toxics
Data Analysis
~t STATEMENT
b IDENTIFIERS/OPEN ENDED TERMS

19 SECURITY CLASS /This Report'
20 SECURITY CLASS i This page,
t. COSATI 1 iclj Group

21 NO OF °AG£S
204
22 PRiCE
(Rev 4-77) PREVIOUS £ = IT
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